Ploting my Caves of Qud data

Having previously cleaned the data it's now time to pull it into a pandas dataframe and get some info from it.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%pylab inline

col_names = ["Name", "End Time", "Game End Time", "Enemy", "x hit", "Damage", "Weapon", "PV", "Pos Dam", "Score", "Turns", "Zones", "Storied Items", "Artifact"]

#read in the data from the text file, setting the seperator between each column as "\t". 
qud = pd.read_csv("Cleaned_Qud_HighScores_1.txt", sep=r"\t+", names = col_names, engine='python')
qud.head()
Populating the interactive namespace from numpy and matplotlib
Out[1]:
Name End Time Game End Time Enemy x hit Damage Weapon PV Pos Dam Score Turns Zones Storied Items Artifact
0 Goethe II Thursday, August 13, 2015 6:04:58 PM 20th of Uru Ux Wahmahcalcalit 0 0 lase beam 0 0 48753 35235 260 1 HE Missile
1 Kant XVIII Sunday, August 30, 2015 7:34:00 PM 27th of Tuum Ut chute crab 1 2 crab claw 7 1d2 40178 37145 222 1 Fix-It spray foam x2
2 O'Brien III Wednesday, September 02, 2015 3:50:10 AM 6th of Tebet Ux Kumukokumu the Stylish, legendary ogre ape 8 51 ape fist 20 3d3 20556 21114 130 0 force bracelet 0 0 <> [no cell]
3 Kant XII Friday, August 28, 2015 11:14:48 PM 7th of Iyur Ut Putus Templar warden 1 3 folded carbide long sword 9 2d5 17061 17066 118 0 electrobow <> ->10 1d6 [no cell]
4 Nietzsche III Wednesday, August 05, 2015 8:00:46 PM 19th of Tishru ii Ux eyeless king crab 6 20 massive king crab claw 20 1d6 16607 16124 115 0 ubernostrum injector <>
In [2]:
#Dropping these two values
print qud.iloc[53].values #Forgot to name my character. Deceided to quit by attacking either Mehmet or Warden Ualraig
print qud.iloc[54].values #Took one step and Ualraig wasted Mehmet. Walked around for a while but quit as I could not longer start the "What's Eating The Watervine? mission

#As these are my two lowest scores I can the set the dataframe to be rows 0 to 53 (does not include 53)
qud = qud[0:53]
qud.tail(3)
['Game summary for' 'Friday, August 21, 2015  11:25:56 PM'
 'died on the 8th of Nivvun Ut' 'Warden Ualraig' 0 0 'Freezes' 0 '0' -1451
 19 1 0 'no artifact']
['Napoleon' 'Monday, August 03, 2015  3:23:01 PM' '18th of Tuum Ut' 'quit'
 0 0 'quit' 0 '0' -1588 95 1 0 'no artifact']
Out[2]:
Name End Time Game End Time Enemy x hit Damage Weapon PV Pos Dam Score Turns Zones Storied Items Artifact
50 Goethe Sunday, August 09, 2015 7:43:13 PM Goethe died on the 22nd of Tuum Ut boar 2 6 bite 7 1d3 -1253 121 3 0 no artifact
51 Malenkov Sunday, August 02, 2015 1:34:01 PM 9th of Tishru ii Ux traipsing mortar 0 0 explosion 0 0 -1318 130 5 0 no artifact
52 Khrushchev III Sunday, August 02, 2015 4:19:46 PM 1st of Nivvun Ut scalding steam 0 0 scalding steam 0 0 -1351 324 4 0 no artifact

Further Cleaning

With the data now pulled into a dataframe there is still a small bit of cleaning to do. Below are three functions to convert the End Time of each game to a datetime, to pull the game month from the Game End Time (I left the day in as at a later time I might check am I more likely to die early or late in the month) and to clean up the Artifact column

In [3]:
import re
from datetime import datetime
from time import strptime

def convert_to_date(date_in):
    date_search = re.search("(\w{6,9}),\s*(\w{3,9})\s*(\d{2}),\s*(\d{4})\s*(\d{1,2}):(\d{2}):(\d{2})\s*(\w{2})", date_in)
    #date_search.group(1) = Day as word(ie Sunday), 2 = Month as word (ie August), 3 = day of month, 4 = year, 5 = hour, 6 = minute, 7 = second, 8 = AM or PM
    
    #In End Time hour is expressed from 1 to 12, ie 1 AM or 1 PM. The below code converts that to 0 to 23, ie 1 or 13
    hour = int(date_search.group(5))

    if date_search.group(8) == "PM":
        if hour == 12:
            pass
        else:
            hour += 12
    
    if date_search.group(8) == "AM":
        if hour == 12:
            hour = 0
            
    
    #Create a datetime. strptime is used to take the first 3 letters of the Month as word and get the int value for that month, ie August = Aug, is month 8 of 12
    new_date = datetime(int(date_search.group(4)), strptime(date_search.group(2)[:3], "%b").tm_mon, int(date_search.group(3)), hour, int(date_search.group(6)), int(date_search.group(7)))
        
    return new_date

qud["End Time"] = qud["End Time"].apply(convert_to_date)
In [4]:
#Pull the month out of Game Time

def convert_game_month(date_in):
    date_search = re.search("of\s*((\w*\s*)*)", date_in)
    return date_search.group(1)

qud["Game End Time"] = qud["Game End Time"].apply(convert_game_month)
In [5]:
def clean_artifacts(artifact):
    x_search = re.search("(x\d+)", artifact) #remove multipliers like "x2"
    if x_search != None:
        artifact = artifact.replace(x_search.group(0), "").strip()
    
    mul_search = re.search("((-?\d+\s*\d+d\d+)+)", artifact) #removes pv and possible weapon damage like "2 1d3"
    if mul_search != None:
        artifact = artifact.replace(mul_search.group(0), "").strip()
        
    artifact = artifact.replace("->", "").replace("<>", "").strip() #removes -> and <> which should be empty from previous cleaning
        
    cell_search = re.search("(\[(\w*\s*)*\])", artifact) #removes [no cell], [shotgun shell] etc
    if cell_search != None:
        artifact = artifact.replace(cell_search.group(0), "").strip()
        
    digit_search = re.search("((\d+\s*)+)", artifact) #removes any remaining digits such as av dv ie 2 4
    if digit_search != None:
        artifact = artifact.replace(digit_search.group(0), "").strip()
        
    return artifact

qud["Artifact"] = qud["Artifact"].apply(clean_artifacts)
qud.head() #print new, clean dataframe
Out[5]:
Name End Time Game End Time Enemy x hit Damage Weapon PV Pos Dam Score Turns Zones Storied Items Artifact
0 Goethe II 2015-08-13 18:04:58 Uru Ux Wahmahcalcalit 0 0 lase beam 0 0 48753 35235 260 1 HE Missile
1 Kant XVIII 2015-08-30 19:34:00 Tuum Ut chute crab 1 2 crab claw 7 1d2 40178 37145 222 1 Fix-It spray foam
2 O'Brien III 2015-09-02 03:50:10 Tebet Ux Kumukokumu the Stylish, legendary ogre ape 8 51 ape fist 20 3d3 20556 21114 130 0 force bracelet
3 Kant XII 2015-08-28 23:14:48 Iyur Ut Putus Templar warden 1 3 folded carbide long sword 9 2d5 17061 17066 118 0 electrobow
4 Nietzsche III 2015-08-05 20:00:46 Tishru ii Ux eyeless king crab 6 20 massive king crab claw 20 1d6 16607 16124 115 0 ubernostrum injector

Sorting by date and by score

With the End Time now cleaned up and converted to a datetime the entire dataframe can be sorted on this column, giving the dataframe in order of my earliest game to my most recent game. I can then print off my highscore progression and after sorting the dataframe by score I can print my 5 highest scores.

In [6]:
sorted_qud = qud.sort(["End Time"]).reset_index(drop = True) #Sort by End Time, reset the index and drop the old index
highscore = -10000
print "Highscore Progression" #Game Number, Name, Date, Score
for score in sorted_qud["Score"]:
    if int(score) > highscore:
        highscore = int(score)
        print "%d %s %s %d" % (
        int(sorted_qud.index[sorted_qud["Score"] == score][0])+ 1, #the index value of the game + 1. My first game is at index 0 so add 1 and this becomes game 1
        sorted_qud["Name"][sorted_qud["Score"] == score].tolist()[0], #Character's name
        sorted_qud["End Time"][sorted_qud["Score"] == score].tolist()[0], #End Time of game
        int(score) #Score
        )
print "\n"        
print "Highest Scores"
sorted_scores = qud.sort(["Score"], ascending = False).reset_index(drop = True) #sort by score
for i in range(5):
    print sorted_scores["Name"].iloc[i], sorted_scores["End Time"].iloc[i], sorted_scores["Score"].iloc[i] #print Name, End Time and Score for first 5 rows
Highscore Progression
1 Stalin 2015-08-01 14:04:38 -1131
2 Stalin 2015-08-01 15:28:05 -71
4 Lenin 2015-08-01 16:08:30 902
16 Khrushchev VIII 2015-08-02 18:19:47 1760
27 Nietzsche III 2015-08-05 20:00:46 16607
29 Goethe II 2015-08-13 18:04:58 48753


Highest Scores
Goethe II 2015-08-13 18:04:58 48753
Kant XVIII 2015-08-30 19:34:00 40178
O'Brien III 2015-09-02 03:50:10 20556
Kant XII 2015-08-28 23:14:48 17061
Nietzsche III 2015-08-05 20:00:46 16607

Ploting my score data

There are now a number of plots I can build using the data I have pulled down. Two simple scatter plots can be made, one containing points for my score and the number of turns taken in each game and another for my score and the number of zones visited. Using the sklearn library I can plot a linear line to each plot and also use this to predict the score of my current game.

Using the dataframe sorted on date I can plot a bar for the score in each game and then plot a 5 game simple moving average. Red vertical lines are added for patch updates to see if my score is affected by these. The lines represent the 4th, 8th and 15/21st of August (I didn't play any games between the 15th and 21st) and I remember it took me a while to get to grips with the game after the 21st of August patch. I blame Ctesiphus.

The final plot is a histogram of my highscores. Not impressive.

In [7]:
from sklearn import linear_model

fig = plt.figure(figsize = (20,10))
p1 = fig.add_subplot(221) # 2x2, plot 1 (top left)
p2 = fig.add_subplot(222) # 2x2, plot 2 (top right)
p3 = fig.add_subplot(223) # 2x2, plot 3 (bottom left)
p4 = fig.add_subplot(224) # 2x2, plot 4 (bottom right)

#Turns to Score
p1.scatter(qud["Turns"], qud["Score"], color="green") #Turns on x axis, score on y axis, color green (this is Qud after all)

X = np.array(qud["Turns"]).reshape(len(qud),1) #variable X is an np.array of the turns, len(qud) rows, 1 column
y= np.array(qud["Score"]).reshape(len(qud),1) #variable y is an np.array of the scores, len(qud) rows, 1 column

turns_score = linear_model.LinearRegression()
turns_score.fit(X, y) #fit turns and score using linear regression

#plot a line with turns on the x axis and predicted score for that many turns from the linear regression model on the y axis
p1.plot(qud["Turns"], turns_score.predict(X), color="red") 
p1.set_title("Score per Turn")
p1.set_xlabel("Turns")
p1.set_ylabel("Score")
p1.axis('tight')

#Zones to Score
p2.scatter(qud["Zones"], qud["Score"], color="green")
X= np.array(qud["Zones"]).reshape(len(qud),1) #Update X to be an np.array of zones, y stays as score above

zones_score = linear_model.LinearRegression()
zones_score.fit(X, y) #fit zones to score

#plot a line with zones on the x axis and predicted score for that many zones from the linear regression model on the x axis
p2.plot(qud["Zones"], zones_score.predict(X), color="red")
p2.set_title("Score per Zone")
p2.set_xlabel("Zones")
p2.set_ylabel("Score")
p2.axis('tight')

#using the sorted by date dataframe plot a bar chart of the scores. sorted_qud.index.values starts at 0, not 1
p3.bar(sorted_qud.index.values, sorted_qud["Score"], color="green")
p3.plot(pd.rolling_mean(sorted_qud["Score"].values, window=5, min_periods=1), color="red", linewidth=2) #plot a 5 game simple moving average

p3.set_title("5 Game Moving Average")
p3.set_xlabel("Game (Vertical lines represent patches: Aug 4, Aug 8, Aug 15/21)")
p3.set_ylabel("Score")
p3.axis('tight')
#These numbers are plotted manually from looking at the dataframe and seeing when was the first game I played on/after each patch release
p3.axvline(24, color = "red", linewidth = 2) #first game on/after Aug 4th
p3.axvline(27, color = "red", linewidth = 2) #first game on/after Aug 8th
p3.axvline(29, color = "red", linewidth = 2) #first game on/after Aug 15th and 21st

#Histogram. Depressing
p4.hist(qud["Score"], bins = 50);
p4.axis('tight')
p4.set_title("Score Frequency")
p4.set_xlabel("Score (50 bins)")
p4.set_ylabel("Frequency")


plt.tight_layout()

Using the linear regression models I can now get the coefficient, intercept and root mean square error for the score per turn line and the score per zone line. Also, as the number of turns is displayed when a game is saved I can calculate how many points my current save game is worth. However, I would not expect this figure to be very accurate due to the small number of points available and I'm not willing to have my character die to check how right the figure is!

In linear regression y = a + Xb where a is the intercept and b is the coefficient. So using the below data:

score = -2171.04135919 + Turns(1.21948537419)

The root mean square error is found by taking the root of the mean_squared_error of the score compared to the predicted score

With a bit of moving around of figures I get to equation turns = (score + intercept)/coefficient which allows me to predict the number of turns needed for 100,000 points and 1,000,000 points. I have a bit of work to do yet! (I expect these numbers to change substantially as the model gets more data points in the high game points range, over half the data points are in the minus range at the moment)

In [8]:
from sklearn.metrics import mean_squared_error
from math import sqrt
print "For Score Per Turn"
print "Total turns multiplied by the coefficient plus the intercept = my score"
print "Coefficient: ", turns_score.coef_[0][0]
print "Intercept: ", turns_score.intercept_[0]
print "RMSE: ", sqrt(mean_squared_error(y, turns_score.predict(np.array(qud["Turns"]).reshape(len(qud),1))))
print "Predicted score from my current game (59924 turns): ", int(turns_score.predict(59924)[0][0])
print "Turns needed for 100,000 points: ", int(math.ceil(((100000 + abs(turns_score.intercept_))/turns_score.coef_)[0][0]))
print "Turns needed for 1,000,000 points: ", int(math.ceil(((1000000 + abs(turns_score.intercept_))/turns_score.coef_)[0][0]))
For Score Per Turn
Total turns multiplied by the coefficient plus the intercept = my score
Coefficient:  1.21948537419
Intercept:  -2171.04135919
RMSE:  1532.50022362
Predicted score from my current game (59924 turns):  70905
Turns needed for 100,000 points:  83783
Turns needed for 1,000,000 points:  821799
In [9]:
print "For Score Per Zone"
print "Total zones visited multiplied by the coefficient plus the intercept = my score"
print "Coefficient: ", zones_score.coef_[0][0]
print "Intercept ", zones_score.intercept_[0]
print "RMSE: ", sqrt(mean_squared_error(y, zones_score.predict(np.array(qud["Zones"]).reshape(len(qud),1))))
For Score Per Zone
Total zones visited multiplied by the coefficient plus the intercept = my score
Coefficient:  187.832983072
Intercept  -2433.84828011
RMSE:  1141.29924083

A look at some of the remaining data

I took in a lot of data into the dataframe but have only looked at End Time, score, zone and turns. As time goes on and I get more entries I may be able to do more with the following bits of data

In [10]:
#Each month mentioned in the Game End Time
game_months = qud["Game End Time"]
print np.unique(game_months)
print len(np.unique(game_months))
['Iyur Ut' 'Kisu Ux' 'Nivvun Ut' 'Shwut Ux' 'Simmun Ut' 'Tebet Ux'
 'Tishru i Ux' 'Tishru ii Ux' 'Tuum Ut' 'Ubu Ut' 'Uru Ux' 'Uulu Ut']
12
In [11]:
#Use groupby to find most mentioned month, ie the month I have died most in. Nivvun Ut is the very first month...
qud['Game End Time'].groupby(qud['Game End Time']).count().order(ascending = False)
Out[11]:
Game End Time
Tuum Ut         8
Uru Ux          7
Nivvun Ut       7
Tishru i Ux     6
Uulu Ut         5
Tishru ii Ux    5
Iyur Ut         4
Shwut Ux        3
Kisu Ux         3
Ubu Ut          2
Tebet Ux        2
Simmun Ut       1
Name: Game End Time, dtype: int64
In [12]:
#Use group by to find the most advanced artifact I held when I died. Lots of no artifacts and lots of artifacts awarded for finishing the first 2 missions in Joppa
qud['Artifact'].groupby(qud['Artifact']).count().order(ascending = False)
Out[12]:
Artifact
no artifact                37
Fix-It spray foam           3
semi-automatic pistol       2
acid gas grenade mk I       2
ubernostrum injector        1
stun gas grenade mk I       1
pump shotgun                1
poison gas grenade mk I     1
force bracelet              1
electrobow                  1
compass bracelet            1
blaze injector              1
HE Missile                  1
Name: Artifact, dtype: int64

I have done slightly more work with the Enemy column. First I print it off as is but then make some changes. Deaths from bleeding were always the result of young ivory. In my early games I took hemophilia as a defect which basicly meant instant death at low toughness levels and no bandages when I stared bleeding, so "bleeding" and "young ivory" are combined into a single group.

Deaths from scalding steam were always the result of fire ants setting the water around me on fire and I, like an idiot, taking a step forward. Only once was there another reason...I set the water on fire with my Flaming Hands and walked into it. Since then I have only used Freezing Hands.

All snapjaws, including the two faction leaders, are put into a single catagory.

"Wahmahcalcalit", "Umchuum", "Duhmahcaluhcal" are evidence my character was suffering confusion when he died, these were 3 wizard faction leaders.

I could do further grouping. Chute crab and king crab could be combined, the two named Snapjaws could be put in with the ogre instead to form a Named or Faction Leader catagory, dawnglider could be added to the fire ant/scalding steam catagory etc.

In [13]:
qud['Enemy'].groupby(qud['Enemy']).count().order(ascending = False)
Out[13]:
Enemy
bleeding                                               9
unknown                                                8
salthopper                                             4
scalding steam                                         3
equimax                                                2
snapjaw scavenger                                      2
jilted lover                                           2
Wahmahcalcalit                                         1
chute crab                                             1
cave spider                                            1
boar                                                   1
young ivory                                            1
dawnglider                                             1
Ruf-ohoubub, the stalwart Snapjaw Bear-baiter          1
Putus Templar warden                                   1
Kumukokumu the Stylish, legendary ogre ape             1
Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-eater    1
Umchuum                                                1
eyeless king crab                                      1
explosion                                              1
fire ant                                               1
giant amoeba                                           1
girshling                                              1
horned chameleon                                       1
napjaw scavenger                                       1
salamander                                             1
scrap shoveler                                         1
snapjaw hunter                                         1
traipsing mortar                                       1
Duhmahcaluhcal                                         1
Name: Enemy, dtype: int64
In [14]:
#create a list called enemies, add new values to it, convert to a dataframe and groupby name
enemies = qud["Enemy"].tolist()
for i in range(len(enemies)):
    name = enemies[i].strip()
    if name in ["Wahmahcalcalit", "Umchuum", "Duhmahcaluhcal"]:
        enemies[i] = "wizard"
    if name in ["snapjaw scavenger", "napjaw scavenger", "snapjaw hunter", "Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-eater", "Ruf-ohoubub, the stalwart Snapjaw Bear-baiter"]:
        enemies[i] = "snapjaw"
    if name in ["young ivory", "bleeding"]:
        enemies[i] = "young ivory/bleeding" 
    if name in ["scalding steam", "fire ant"]:
        enemies[i] = "fire ant/scalding steam"

enemy_df = pd.DataFrame(enemies, columns=["Name"])
enemy_df['Name'].groupby(enemy_df['Name']).count().order(ascending = False)
Out[14]:
Name
young ivory/bleeding                          10
unknown                                        8
snapjaw                                        6
salthopper                                     4
fire ant/scalding steam                        4
wizard                                         3
jilted lover                                   2
equimax                                        2
explosion                                      1
Putus Templar warden                           1
boar                                           1
cave spider                                    1
chute crab                                     1
dawnglider                                     1
giant amoeba                                   1
eyeless king crab                              1
girshling                                      1
horned chameleon                               1
salamander                                     1
scrap shoveler                                 1
traipsing mortar                               1
Kumukokumu the Stylish, legendary ogre ape     1
Name: Name, dtype: int64

There are a number of highscores that have an empty line where the death description should be and these are marked unknown. Also, others will just read "died from bleeding". Here bleeding is added as both the enemy name and the weapon.

Below is a look at the weapons that have killed my characters. Rending mandibles. Giant pseudopod. The things I've seen.

In [15]:
qud['Weapon'].groupby(qud['Weapon']).count().order(ascending = False)
Out[15]:
Weapon
bleeding                     9
unknown                      8
rending mandibles            4
bite                         4
explosion                    3
scalding steam               3
lase beam                    2
bronze two-handed sword      2
thorns                       2
Umumerchacal                 1
ape fist                     1
carbide battle axe           1
claw                         1
crab claw                    1
flames                       1
fangs                        1
fire                         1
folded carbide long sword    1
giant pseudopod              1
impalement                   1
iron dagger                  1
massive king crab claw       1
scrap shovel                 1
steel battle axe             1
Tusks                        1
Name: Weapon, dtype: int64

Below is the complete dataframe sorted by date

In [16]:
sorted_qud
Out[16]:
Name End Time Game End Time Enemy x hit Damage Weapon PV Pos Dam Score Turns Zones Storied Items Artifact
0 Stalin 2015-08-01 14:04:38 Nivvun Ut Umchuum 2 4 Umumerchacal 0 0 -1131 362 3 0 poison gas grenade mk I
1 Stalin 2015-08-01 15:28:05 Uru Ux salthopper 3 9 rending mandibles 11 1d4 -71 3412 30 0 no artifact
2 Stalin 2015-08-01 15:41:26 Tishru i Ux explosion 0 0 explosion 0 0 -224 1061 8 0 no artifact
3 Lenin 2015-08-01 16:08:30 Iyur Ut equimax 3 9 bite 8 2d2 902 1019 12 0 no artifact
4 Malenkov 2015-08-01 16:18:54 Tishru i Ux Ruf-ohoubub, the stalwart Snapjaw Bear-baiter 2 10 bronze two-handed sword 4 1d8 -1155 687 8 0 no artifact
5 Malenkov 2015-08-01 16:35:17 Tuum Ut napjaw scavenger 0 0 explosion 0 0 -1004 911 12 0 no artifact
6 Malenkov 2015-08-02 13:34:01 Tishru ii Ux traipsing mortar 0 0 explosion 0 0 -1318 130 5 0 no artifact
7 Khrushchev 2015-08-02 14:13:37 Uru Ux bleeding 0 0 bleeding 0 0 -531 1863 17 0 no artifact
8 Khrushchev 2015-08-02 15:52:52 Tishru ii Ux Groubuubu-wof-wofuz, the stalwart Snapjaw Tot-... 2 11 carbide battle axe 5 2d3 708 2789 21 0 no artifact
9 Khrushchev II 2015-08-02 16:13:32 Uru Ux bleeding 0 0 bleeding 0 0 13 1640 9 0 no artifact
10 Khrushchev III 2015-08-02 16:19:46 Nivvun Ut scalding steam 0 0 scalding steam 0 0 -1351 324 4 0 no artifact
11 Khrushchev IV 2015-08-02 16:35:26 Nivvun Ut snapjaw scavenger 1 1 iron dagger 2 1d4 -936 1134 12 0 no artifact
12 Khrushchev V 2015-08-02 16:45:07 Uulu Ut salthopper 4 10 rending mandibles 10 1d4 -918 510 6 0 no artifact
13 Khrushchev VI 2015-08-02 17:12:49 Tuum Ut fire ant 0 0 flames 0 0 -126 1705 21 0 no artifact
14 Khrushchev VII 2015-08-02 17:17:56 Uru Ux salamander 1 3 bite 3 1d3 -1127 403 5 0 no artifact
15 Khrushchev VIII 2015-08-02 18:19:47 Tuum Ut bleeding 0 0 bleeding 0 0 1760 4198 25 0 no artifact
16 Khrushchev IX 2015-08-03 13:56:40 Tishru i Ux bleeding 0 0 bleeding 0 0 -1143 336 4 0 no artifact
17 Khrushchev X 2015-08-03 13:59:50 Tishru ii Ux unknown 0 0 unknown 0 0 -1209 287 4 0 no artifact
18 Khrushchev XI 2015-08-03 15:18:32 Kisu Ux unknown 0 0 unknown 0 0 1136 4121 21 0 blaze injector
19 Napoleon 2015-08-03 15:41:37 Uulu Ut bleeding 0 0 bleeding 0 0 -547 1536 11 0 no artifact
20 Napoleon II 2015-08-03 15:57:39 Tishru i Ux bleeding 0 0 bleeding 0 0 -590 1298 11 0 no artifact
21 Napolen III 2015-08-03 16:20:16 Shwut Ux dawnglider 0 0 fire 0 0 -543 1773 16 0 no artifact
22 Napoleon IV 2015-08-03 16:28:33 Uulu Ut salthopper 2 5 rending mandibles 10 1d4 -1171 275 4 0 no artifact
23 Napoleon V 2015-08-03 16:31:59 Ubu Ut bleeding 0 0 bleeding 0 0 -1175 320 3 0 no artifact
24 Nietzsche 2015-08-04 20:18:00 Tuum Ut horned chameleon 1 4 Tusks 4 2d3 1503 3719 27 0 no artifact
25 Nietzsche II 2015-08-04 20:25:57 Simmun Ut jilted lover 2 3 thorns 5 1d4 -1246 136 3 0 no artifact
26 Nietzsche III 2015-08-05 20:00:46 Tishru ii Ux eyeless king crab 6 20 massive king crab claw 20 1d6 16607 16124 115 0 ubernostrum injector
27 Goethe 2015-08-09 19:43:13 Tuum Ut boar 2 6 bite 7 1d3 -1253 121 3 0 no artifact
28 Goethe II 2015-08-13 18:04:58 Uru Ux Wahmahcalcalit 0 0 lase beam 0 0 48753 35235 260 1 HE Missile
29 Kant 2015-08-21 23:08:22 Tuum Ut giant amoeba 2 4 giant pseudopod 10 1d3 2372 3013 21 0 no artifact
30 Kant II 2015-08-21 23:14:45 Ubu Ut snapjaw hunter 3 16 bronze two-handed sword 4 1d8 -1214 145 4 0 no artifact
31 Kant II 2015-08-21 23:23:56 Shwut Ux scrap shoveler 5 8 scrap shovel 15 1d2 -986 720 6 0 no artifact
32 Kant III 2015-08-21 23:25:01 Kisu Ux bleeding 0 0 bleeding 0 0 -1252 105 3 0 no artifact
33 Kant IV 2015-08-22 00:00:38 Iyur Ut snapjaw scavenger 1 6 steel battle axe 3 1d6 1393 3347 16 0 acid gas grenade mk I
34 Kant V 2015-08-22 18:22:36 Tishru i Ux unknown 0 0 unknown 0 0 6662 8118 47 0 semi-automatic pistol
35 Kant VI 2015-08-27 16:36:27 Shwut Ux young ivory 0 0 impalement 0 0 -911 613 7 0 no artifact
36 Kant VII 2015-08-27 16:50:50 Uulu Ut unknown 0 0 unknown 0 0 -243 770 8 0 no artifact
37 Kant VIII 2015-08-27 20:01:17 Tishru i Ux scalding steam 0 0 scalding steam 0 0 7848 7719 35 0 Fix-It spray foam
38 Kant IX 2015-08-27 20:51:15 Iyur Ut jilted lover 1 2 thorns 5 1d4 -80 1807 13 0 acid gas grenade mk I
39 Kant X 2015-08-27 21:17:34 Kisu Ux cave spider 1 2 fangs 2 1d2 265 2287 16 0 no artifact
40 Kant XI 2015-08-27 23:14:09 Uru Ux scalding steam 0 0 scalding steam 0 0 9971 11105 60 0 Fix-It spray foam
41 Kant XII 2015-08-28 23:14:48 Iyur Ut Putus Templar warden 1 3 folded carbide long sword 9 2d5 17061 17066 118 0 electrobow
42 Kant XIII 2015-08-29 00:05:24 Tebet Ux girshling 1 6 claw 2 1d6 1849 2853 18 0 pump shotgun
43 Kant XIV 2015-08-29 00:23:09 Tuum Ut bleeding 0 0 bleeding 0 0 -367 2230 14 0 no artifact
44 Kant XV 2015-08-29 00:42:44 Nivvun Ut unknown 0 0 unknown 0 0 -434 1332 10 0 no artifact
45 Kant XVI 2015-08-29 00:46:03 Uulu Ut equimax 2 5 bite 9 2d2 -1120 401 5 0 no artifact
46 Kant XVII 2015-08-29 01:44:21 Tishru ii Ux unknown 0 0 unknown 0 0 3169 7239 37 0 compass bracelet
47 Kant XVIII 2015-08-30 19:34:00 Tuum Ut chute crab 1 2 crab claw 7 1d2 40178 37145 222 1 Fix-It spray foam
48 O'Brien 2015-09-02 00:03:29 Nivvun Ut unknown 0 0 unknown 0 0 -1174 287 5 0 no artifact
49 O'Brien II 2015-09-02 00:53:32 Nivvun Ut Duhmahcaluhcal 0 0 lase beam 0 0 5471 6356 42 0 stun gas grenade mk I
50 O'Brien III 2015-09-02 03:50:10 Tebet Ux Kumukokumu the Stylish, legendary ogre ape 8 51 ape fist 20 3d3 20556 21114 130 0 force bracelet
51 O'Brien IV 2015-09-02 12:47:21 Uru Ux salthopper 3 7 rending mandibles 11 1d4 199 1810 14 0 semi-automatic pistol
52 O'Brien V 2015-09-02 12:58:51 Nivvun Ut unknown 0 0 unknown 0 0 -618 1538 14 0 no artifact
In [ ]: